Import packages

# Import necessary libraries
from plotly.subplots import make_subplots
import plotly.graph_objects as go
import plotly.io as pio
import pandas as pd
import plotly.express as px
import plotly, os, joblib
import numpy as np
import pickle

Custom Functions for Generating Plots

# Function to generate the plot
def generatePlot(plotName, rows, cols, data, width, height, vertical_spacing, horizontal_spacing,
                 title_font, marker_size, label_size, tick_size, plot_title, line_color):
    # Sorting the data
    df_sorted = data

    # Setting up the subplot structure
    specs = []
    for row in range(1, rows + 1):
        a = []
        for col in range(1, cols + 1):
            a.append({"type": "polar"})
        specs.append(a)

    # Creating the subplots
    fig = make_subplots(rows=rows, cols=cols, vertical_spacing=vertical_spacing,
                        subplot_titles=[i.replace('__', '-') for i in df_sorted.index.tolist()],
                        horizontal_spacing=horizontal_spacing, specs=specs)

    # Adding traces to the subplots
    row = 1
    col = 1
    for model in df_sorted.index:
        name = []
        value = []

        model_score = df_sorted.loc[model]
        for score in model_score.index:
            if score == "model":
                continue
            name.append(score)
            value.append(model_score.loc[score] * 100)

        fig_tem = go.Scatterpolar(r=value, name=model, dtheta=20,
                                 theta=name, fill='toself',
                                 line_color=line_color)
        fig.add_trace(fig_tem,
                      row=row, col=col)

        if col == cols:
            col = 1
            row += 1
        else:
            col += 1

    # Updating layout and annotations
    for i in fig['layout']['annotations']:
        i['font'] = dict(size=title_font, family="Arial", color='black')
        i['borderpad'] = 5

    # Finalizing the layout
    fig.update_layout(width=width, height=height, font_size=label_size, template="plotly_white",
                      font_family="Arial",
                      showlegend=False, margin=dict(t=30, b=15, r=40, l=40,))
    fig.update_layout(title={'text': plot_title, 'y': 0.995, 'x': 0.5,
                            'xanchor': 'center', 'yanchor': 'top',
                            "font_family": "Arial", "font_size": 10})

    # Updating polar axis
    fig.update_polars(radialaxis=dict(
        visible=True, nticks=7, range=[30, 100],
        tickfont=dict(size=tick_size)
    ),
        angularaxis=dict(showticklabels=False, ticks='', linewidth=0.2, showline=True, linecolor='black'))

    # Updating traces
    fig.update_traces(marker=dict(size=marker_size, line_color="black", color=px.colors.sequential.Viridis),
                      selector=dict(type='scatterpolar'))
    fig.update_polars(angularaxis=dict(showticklabels=True))
    return(fig) 
    
    
    # Function to generate spyder plots
def getSpyderPlot(results, cvName, plot_title, line_color):
    # Convert results to DataFrame
    df = pd.DataFrame({'Accuracy': results["Acc"],
                       'Balanced Acc': results["Bal_acc"],
                       'F1': results["F1"],
                       'Recall': results["recall"],
                       'Precision': results["precision"],
                       'Avg precision': results["average_precision"],
                       'roc_auc': results["roc_auc"],
                       "model": results["model"]}, index=results["model"])

    # Sort the DataFrame
    df_sorted = df.sort_values('F1')
    # Generate the spyder plot
    return(generatePlot(cvName, 2, 4, df_sorted, 850, 450, 0.08, 0.09, 10, 4, 9, 9, plot_title, line_color))

Results Folder

# Specify the path to the results folder downloaded from GitHub. 
#resultsFolder='/path/to/results/folder'
resultsFolder='/Users/akshay/Desktop/prof_katia_plots/pain-paper/MLcps-paper/MLcps/generateManuPlots/results'

1. CLL Dataset

Setup

# change directory to the CLL results folder.
# All the plots will be saved here.
os.chdir(os.path.join(resultsFolder,"CLL"))

# load dataset
results_whole = pickle.load(open("results_whole.pickle",'rb'))

Figure S1A

getSpyderPlot(results_whole,"result_whole","CLL","#B9E4E8")

2. Cervical Dataset

Setup

# change directory to the cervical results folder.
# All the plots will be saved here.
os.chdir(os.path.join(resultsFolder,"cervical"))

# load dataset
results_whole = pickle.load(open("results_whole.pickle",'rb'))

Figure 2A

getSpyderPlot(results_whole,"result_whole","Cervical","#B9E4E8")

3. TCGA miRNA Dataset

Setup

# change directory to the TCGA miRNA results folder.
# All the plots will be saved here.
os.chdir(os.path.join(resultsFolder,"TCGA-BRCA_miRNA"))

# load dataset
results_whole = pickle.load(open("results_whole.pickle",'rb'))
results_test = pickle.load(open("results_test.pickle",'rb'))

Figure 1B

getSpyderPlot(results_whole,"result_whole","TCGA-BRCA-miRNA","#B9E4E8")

Figure 1C

getSpyderPlot(results_whole,"result_test","TCGA-BRCA-miRNA","#8CBFAA")

4. TCGA mRNA Dataset

Setup

# change directory to the TCGA mRNA results folder.
# All the plots will be saved here.
os.chdir(os.path.join(resultsFolder,"TCGA-BRCA_mRNA"))

# load dataset
results_whole = pickle.load(open("results_whole.pickle",'rb'))
results_test = pickle.load(open("results_test.pickle",'rb'))

Figure 2B

getSpyderPlot(results_whole,"result_whole","TCGA-BRCA-mRNA","#B9E4E8")

Figure 2D

getSpyderPlot(results_whole,"result_test","TCGA-BRCA-mRNA","#8CBFAA")